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Photosynthetic characteristics of tall trees play important roles in improving the accuracy of ecosystem models, but they are laborious to be accurately measured or estimated owing to the influence of multiple factors. To clarify the main drivers of vertical and seasonal patterns of leaf photosynthetic characteristics of young planted Larix Olgensis trees, we collected data on the photosynthetic, morphological, and meteorological characteristics by a long-term observation throughout the entire growing season. Vertical and seasonal patterns of leaf photosynthetic characteristics and their impact factors were analyzed. Results showed that maximum net CO2 assimilation (Amax), light saturated stomatal conductance (gs-sat), respiration rate (RD), needle mass per area (NMA), and ratio of needle length to needle width (rlw) all significantly and negatively correlated with relative depth into crown (RDINC), that was caused by the adaptive alteration of mesophyll tissue to the differed light intensity and humidity. Amax and gs-sat both showed a similar 'parabolic' seasonal trend, that was not only affected by the variation of environment but also the leaf economic spectrum, such as NMA. Our results suggested that spatiotemporal variations of crown photosynthetic characteristics were directly influenced by leaf economic spectrum but fundamentally affected by the long-term acclimation to surrounding environmental factors. This is helpful to optimize the crown photosynthesis model to assess instantaneous or even long-term photosynthetic production, in order to clarify the balance of supply and demand within crown, and further guide the effective pruning for individual trees.

ABSTRACT: Objectives: Staphylococcus aureus, especially methicillin-resistant S. aureus (MRSA), is an important zoonotic microorganism that increasingly causes public health concern worldwide. The objective of this study was to determine the prevalence and transmission of S. aureus in duck farms and evaluate its antimicrobial resistance and genetic characteristics. Methods: The samples associated with ducks, feeders, and the environment were collected on 14 duck farms from four areas in Guangdong, China, from 2020 to 2021. All isolates were subjected to antimicrobial susceptibility testing. A comprehensive epidemiological survey of S. aureus was conducted by S. aureus protein A typing and whole-genome sequencing. Results: A total of 560 samples were collected. The prevalence rate of MRSA among ducks (8.1%, 11 of 135) was higher compared with that in environmental samples. OptrA-positive ST398-t034 MRSA were first detected from duck farms in China. A total of 79.3% (34 of 46) S. aureus isolates showed multidrug-resistant phenotypes. Notably, some isolates carried multidrug-resistant genes encoding macrolide–lincosamide–streptogramin B, pleuromutilin–pleuromutilin–streptogramin A, and oxazolidinone. Analysis of the virulence genes revealed that the MRSA isolates carried genes encoding gamma-hemolysin, enterotoxin, and leukocidin. ST9-t899 is a primary clonal lineage among duck- and environment-associated MRSA. Single-nucleotide polymorphism analysis showed the potential contamination relationship of optrA-positive ST2308 MRSA isolates carrying the gamma-hemolysin genes and the leukocidin virulence genes between airborne dust and sick ducks. Conclusion: The contamination of MRSA, especially optrA-positive MRSA, between food animals and the environment is a growing public health concern worldwide. Based on One Health principles, continuous surveillance of MRSA is urgently needed.

Abstract Key message The diameter growth of Dahurian larch (Larix gmelini Rupr.) and white birch (Betula platyphylla Suk.) species in secondary forest of Northeast China was not only influenced by biological factors such as tree size and stand characteristics, but also significantly affected by topographic and climatic factors such as temperature and precipitation. It is necessary to consider the abiotic factors in simulating the diameter growth. Context Climate change, such as global temperature rise, increased frequency of extreme weather events, and rising sea levels, has put forest ecosystems in an unstable state and has an impact on species composition, growth harvest, productivity and other functions of forests. And this impact varies in climate scenarios, regions and forest types. Aims To gain a comprehensive understanding of the adaptation for key species to their environment in secondary forests in Northeast China, the diameter growth responses of Dahurian larch and white birch to biotic and abiotic factors were simulated to assess the effects of climate on diameter growth. Methods China’s National Forest Continuous Inventory (NFCI) data from 2005 to 2015 were used to develop linear mixed-effects diameter growth models with plot-level random effects, and leave-one-out cross-validation was applied to evaluate the developed models. At the beginning of modeling, correlation analysis and best-subset regression were used to analyze the correlation between the diameter increment and the biotic and abiotic factors. Results (i) Sorting the categories of predictors in descending order based on the relative importance of the significant predictors, diameter growth of Dahurian larch was affected by competition, tree size, topographic conditions, stand attributes, diversity index, and climate factors, while the white birch species was affected by competition, tree size, stand attributes, climate factors, diversity index, and topographic conditions; (ii) the plot-level mixed-effects model, which achieved better fit and prediction performance than did basic linear models of individual-tree diameter growth in the cases of prediction calibration, was preferable for modeling individual-tree diameter growth; (iii) the prediction accuracy of the mixed-effects model increased gradually with increasing size of calibration sample, and the best sampling strategy was the use of nine random trees to calibrate and make predictions with the mixed-effects model for the larch and birch species; (iv) Dahurian larch was dominant in terms of interspecific competition, and the growth of this species was enhanced when it was grown with the birch. Conclusion In addition to biotic factors such as tree size and stand characteristics, the impact of climate on the growth of Dahurian larch and white birch should be considered in future management policies.

Leaf mass per area (LMA) is a key structural parameter that reflects the functional traits of leaves and plays a vital role in simulating the material and energy cycles of plant ecosystems. In this study, vertical whorl-by-whorl sampling of LMA was conducted in a young Larix principis-rupprechtii plantation during the growing season at the Saihanba Forest Farm. The vertical and seasonal variations in LMA were analysed. Subsequently, a predictive model of LMA was constructed. The results revealed that the LMA varied significantly between different crown whorls and growing periods. In the vertical direction of the crown, the LMA decreased with increasing crown depth, but the range of LMA values from the tree top to the bottom was, on average, 30.4 g/m2, which was approximately 2.5 times greater in the fully expanded phase than in the early leaf-expanding phase. During different growing periods, the LMA exhibited an allometric growth trend that increased during the leaf-expanding phase and then tended to stabilize. However, the range of LMA values throughout the growing period was, on average, 40.4 g/m2. Among the univariate models, the leaf dry matter content (LDMC) performed well (adjusted determination coefficient (Ra2) = 0.45, root mean square error (RMSE) = 13.48 g/m2) in estimating the LMA. The correlation between LMA and LDMC significantly differed at different growth stages and at different vertical crown whorls. The dynamic predictive model of LMA constructed with the relative depth in the crown (RDINC) and date of the year (DOY) as independent variables was reliable in both the assessments (Ra2 = 0.68, RMSE = 10.25 g/m2) and the validation (absolute mean error (MAE) = 8.05 g/m2, fit index (FI) = 0.682). Dynamic simulations of crown LMA provide a basis for elucidating the mechanism of crown development and laying the foundation for the construction of an ecological process model.

Ti-15-3 titanium alloy has been widely used as a connecting material for aerospace structural parts. However, the non-uniform pitting caused by salt spray corrosion and the limitations of the conventional electrochemical techniques in predicting localized corrosion seriously prevent the practical application of Ti-15-3 alloy. Herein, the electrochemical noise based on discrete wavelet transform and Hilbert-Huang transform is employed for studying pitting corrosion of both the untreated and polished Ti-15-3 alloy under different neutral salt spray time. Combining with the further analysis of the electrochemical impedance spectroscopy, a complementary evaluation is used to analyze the effect of surface roughness on pitting corrosion behaviors of Ti-15-3 alloy systematically. It is found that pitting forms of both surface roughness electrodes exposed to different salt spray time are successfully distinguished, and the polished Ti-15-3 alloy shows better corrosion resistance. Microstructural characterization confirms the inferences from electrochemical noise analyses, the results indicate that the smooth surface of the polished Ti-15-3 alloy is less vulnerable to the coexistence of metastable pitting and the growth of stable pitting, and effectively improves the resistance to pitting corrosion of Ti-15-3 alloy. Furthermore, a possible mechanism for the triggering of pitting corrosion of Ti-15-3 alloy with different roughness in salt spray environment is proposed. This work will provide significant insights for rational design of Ti-15-3 alloy used in the tropical ocean facilities to avoid the attack of pitting corrosion.

Livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) is an important zoonotic microorganism that is increasingly causing public health concern worldwide. The objective of this study was to determine the transmission and occurrence of MRSA in a slaughterhouse environment and evaluate its antimicrobial resistance and genetic characterization. In this study, we conducted a comprehensive epidemiological survey of S. aureus by spa typing and whole-genome sequencing (WGS) of samples obtained from the pork production chain, the environment, and community residents. To clarify the evolutionary relationships of MRSA sequence type (ST) 398 in this study and global isolates, 197 published whole-genome sequences data of MRSA ST398 strains were downloaded from the GenBank database and included in the phylogenetic analysis. A total of 585 porcine samples (snout and carcass swabs), 78 human nasal samples, and 136 environmental samples were collected. The MRSA isolates were detected at higher frequencies in samples from swine (15.0%) than carcasses (10.0%), slaughterhouse workers (8.0%), community residents (0%), and environment samples (5.9%). The spa typing results showed that t571 accounted for a higher proportion than other spa types. Closely related isolates from the samples of swine, slaughterhouse workers, carcasses, carrier vehicle, and surrounding fishpond water indicate that MRSA ST398 strains may spread among swine, humans, and the environment. MRSA ST398-t571 isolates were genetically different from global strains, except for two Korean isolates, which showed genetic closeness with it. In addition, a MRSA ST398 isolate recovered from an infected patient in Europe differed by only 31 SNPs from the airborne dust-associated strain isolated in this study, thereby suggesting potential transmission among different countries. Antimicrobial susceptibility testing results demonstrated that 99.0% (96/97) of MRSA and 95.1% (231/243) of methicillin-sensitive S. aureus (MSSA) showed multidrug-resistant (MDR) phenotypes. According to WGS analysis, the poxtA-carrying segment (IS431mec-optrA-IS1216-fexB-IS431mec) was reported in MRSA ST398 isolates for the first time. The coexistence of cfr and optrA in a plasmid was first detected in MRSA ST398. The potential transmission of MRSA among humans, animals, and the environment is a cause for concern. The emergence and transmission of LA-MRSA ST398 with high levels of resistance profiles highlight the urgent need for LA-MRSA surveillance.

Abstract The intimate relationship between humans and companion animals causes a unique and critical aspect of antimicrobial resistance in humans. However, a comprehensive analysis of antimicrobial resistance between companion animals and their owners is lacking. Here, we chose 13 owned dogs and 16 owners as well as 22 kennel dogs to analyze the effect of an intimate relationship between owned dogs and owners on their gut microbiome, antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs) and study the correlation of antimicrobial resistance between dogs and their owners in families by metagenomics. Dog gut microbiota had a higher abundance and diversity of ARGs while owners had a higher diversity of taxonomy. In the owned dog gut microbial community, ARG and MGE compositions were significantly more similar to the owner's gut microbiota than those of others. From the perspective of families, there was a strong correlation between macrolide resistance genes between dogs and their owners. In conclusion, our study demonstrated the correlation of ARGs between dogs and their owners at a community‐wide level. These findings can alarm the use of antibiotics in companion animals, which implies the potential to harbor antimicrobial resistance and threaten public health.

ABSTRACT The chicken gut microbiota, as a reservoir of antibiotic resistance genes (ARGs), poses a high risk to humans and animals worldwide. Yet a comprehensive exploration of the chicken gut antibiotic resistomes remains incomplete. In this study, we established the largest chicken gut resistance gene catalogue to date through metagenomic analysis of 629 chicken gut samples. We found significantly higher abundance of ARGs in the Chinese chicken gut than that in the Europe. tetX, mcr, and blaNDM, the genes resistant to antibiotics of last resort for human and animal health, were detected in the Chinese chicken gut. The abundance of ARGs was linearly correlated with that of mobile genetic elements (MGEs). The host-tracking analysis identified Escherichia, Enterococcus, Staphylococcus, Klebsiella, and Lactobacillus as the major ARG hosts. Especially, Lactobacillus, an intestinal probiotic, carried multiple drug resistance genes, and was proportional to ISLhe63, highlighting its potential risk in agricultural production processes. We first established a reference gene catalogue of chicken gut antibiotic resistomes. Our study helps to improve the knowledge and understanding of chicken antibiotic resistomes for knowledge-based sustainable chicken meat production. IMPORTANCE The prevalence of antibiotic resistance genes in the chicken gut environment poses a serious threat to human health; however, we lack a comprehensive exploration of antibiotic resistomes and microbiomes in the chicken gut environment. The results of this study demonstrate the diversity and abundance of antibiotic resistance genes and flora in the chicken gut environment and identify a variety of potential hosts carrying antibiotic resistance genes. Further analysis showed that mobile genetic elements were linearly correlated with antibiotic resistance genes abundance, implying that we should pay attention to the role played by mobile genetic elements in antibiotic resistance genes transmission. We established a reference genome of gut antibiotic resistance genes in chickens, which will help to rationalize the use of drugs in poultry farming.

BackgroundLong noncoding RNAs (lncRNAs) have emerged to have irreplaceable roles in the epigenetic regulation of cancer progression, but their biological functions in colorectal cancer (CRC) remain unclear.MethodsLncRNA expression profiles in CRC tissue and their normal counterpart were explored. Through gain and loss of function approaches, the role of lncRNA PTTG3P was validated in relevant CRC cells and subcutaneous tumor model. The correlations of PTTG3P expression with clinical outcomes were assessed.ResultsPTTG3P was upregulated in CRC tissues and was closely correlated with unsatisfactory prognosis. PTTG3P facilitated glycolysis and proliferation, and the transcriptional regulator YAP1 was necessary for PTTG3P-induced proliferation. Mechanistically, the N6-methyladenosine (m6A) subunit METTL3 increased PTTG3P expression by influencing its stability, while insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) could identify PTTG3P m6A methylation status and bind to it. IGF2BP2 knockdown partly recovered PTTG3P expression induced by METTL3, indicating that METTL3-regulated PTTG3P expression depended on the presence of IGF2BP2. Finally, rescue assays validated the critical role of the METTL3/PTTG3P/YAP1 axis on CRC proliferation.ConclusionsPTTG3P is an independent prognostic biomarker for CRC. The METTL3/PTTG3P/YAP1 axis promotes the progression of CRC and is a promising treatment target.

In the process of modeling height–diameter models for Mongolian pine (Pinus sylvestris var. mongolica), the fitting abilities of six models were compared: (1) a basic model with only diameter at breast height (D) as a predictor (BM); (2) a plot-level basic mixed-effects model (BMM); (3) quantile regression with nine quantiles based on BM (BQR); (4) a generalized model with stand or competition covariates (GM); (5) a plot-level generalized mixed-effects model (GMM); and (6) quantile regression with nine quantiles based on GM (GQR). The prediction bias of the developed models was assessed in cases of total tree height (H) predictions with calibration or without calibration. The results showed that extending the Chapman–Richards function with the dominant height and relative size of individual trees improved the prediction accuracy. Prediction accuracy was improved significantly when H predictions were calibrated for all models, among which GMM performed best because random effect calibration provided the lowest prediction bias. When at least 8% of the trees were selected from a new plot, relatively accurate and low-cost prediction results were obtained by all models. When predicting the H values of Mongolian pine for a new stand, GMM and BMM were preferable if there were available height measurements for calibration; otherwise, GQR was the best choice.

The area of larch (Larix gmelinii) plantations in northeast China has increased following the massive afforestation program implemented by China’s central government to provide biomass for global carbon sink and the development of industrial sectors. Hence, a better comprehension of equations used to characterize the estimation of regional biomass is much needed. A total of 301 sample trees of both planted and natural larch from northeast China were used to construct the additive systems of general and dummy variable models using weighted nonlinear seemingly unrelated regression (NSUR) to investigate the effect of forest origin on tree compartments and total biomass equations. Three different combinations of predictors were used in the two additive systems, and each biomass equation has its own specific weighting function to achieve the homoscedasticity in model residuals. Biomass partitioning, root-to-shoot ratio (RSR), and carbon concentration between the two origins were also analyzed. The results indicated that (1) the additive system of origin-based dummy variable model clearly outperformed the general additive model according to their respective predictors’ combinations and the difference between them were found to be significant; (2) based on the jackknife models’ validation, the two additive systems delivered good biomass predictions, of which the best models’ R2 were 0.939, 0.987, 0.910, 0.878, and 0.989 for root, stem, branch, foliage, and total biomass equations, respectively; (3) biomass allocations were varied between the two origins, and the RSR was higher in natural forests (0.32 ± 0.16) compared to plantations (0.25 ± 0.06) both averagely (±SD) and entirely across all diameter classes; (4) the mean carbon concentrations of root, stem, branch, and foliage observed in naturally regenerated trees were 1.81%, 2.90%, 1.23%, and 2.30% higher than those in plantations, respectively. The newly developed additive systems of origin-based equations can provide a more precise estimation for single-tree biomass of Larix gmelinii in China’s National Forest Inventory.

The relative growth rate (RGRnv) is the standardized measurement of forest growth, whereby excluding the size differences between individuals allows their performance to be compared equally. The RGRnv model was developed using the National Forest Inventory (NFI) data on the Daxing’an Mountains, in Northeast China, which contain Dahurian larch (Larix gmelinii Rupr.), white birch (Betula platyphylla Suk.), and mixed coniferous–broadleaf forests. Four predictor variables—i.e., quadratic mean diameter (Dq), stand basal area (G), average tree height (Ha), and altitude (A)—and four different methods—i.e., the nonlinear mixed-effects models (NLME), three nonlinear quantile regression (NQR3), five nonlinear quantile regression (NQR5), and nine nonlinear quantile regression (NQR9) models—were used in this study. All the models were validated using the leave-one-out method. The results showed that (1) the mixed coniferous–broadleaf forest presented the highest RGRnv; (2) the RGRnv was negatively correlated with the four predictors, and the heteroscedasticity reduced significantly after the weighting function was integrated into the models; and (3) the quantile regression models performed better than NLME, and NQR9 outperformed both NQR3 and NQR5. To make more accurate predictions, parameters of the adjusted mixed-effects and quantile regression models should be recalculated and localized using sampled RGRnv in each region and then applied to predict all the other RGRnv of plots. MAPE% indicates the mean absolute percentage error. The values were stable when the sample numbers were greater than or equal to six across the three forest types, which showed relatively accurate and lowest-cost prediction results.

As a core content of forest management, the height to crown base (HCB) model can provide a theoretical basis for the study of forest growth and yield. In this study, 8364 trees of Larix olgensis within 118 sample plots from 11 sites were measured to establish a two-level nonlinear mixed effect (NLME) HCB model. All predictors were derived from an unmanned aerial vehicle light detection and ranging (UAV-LiDAR) laser scanning system, which is reliable for extensive forest measurement. The effects of the different individual trees, stand factors, and their combinations on the HCB were analyzed, and the leave-one-site-out cross-validation was utilized for model validation. The results showed that the NLME model significantly improved the prediction accuracy compared to the base model, with a mean absolute error and relative mean absolute error of 0.89% and 9.71%, respectively. In addition, both site-level and plot-level sampling strategies were simulated for NLME model calibration. According to different prediction scale and accuracy requirements, selecting 15 trees randomly per site or selecting the three largest trees and three medium-size trees per plot was considered the most favorable option, especially when both investigations cost and the model’s accuracy are primarily considered. The newly established HCB model will provide valuable tools to effectively utilize the UAV-LiDAR data for facilitating decision making in larch plantations management.

Accurate estimation of tree biomass is required for accounting for and monitoring forest carbon stocking. Allometric biomass equations constructed by classical statistical methods are widely used to predict tree biomass in forest ecosystems. In this study, a Bayesian approach was proposed and applied to develop two additive biomass model systems: one with tree diameter at breast height as the only predictor and the other with both tree diameter and total height as the predictors for planted Korean larch (Larix olgensis Henry) in the Northeast, P.R. China. The seemingly unrelated regression (SUR) was used to fit the simultaneous equations of four tree components (i.e., stem, branch, foliage, and root). The model parameters were estimated by feasible generalized least squares (FGLS) and Bayesian methods using either non-informative priors or informative priors. The results showed that adding tree height to the model systems improved the model fitting and performance for the stem, branch, and foliage biomass models, but much less for the root biomass models. The Bayesian methods on the SUR models produced narrower 95% prediction intervals than did the classical FGLS method, indicating higher computing efficiency and more stable model predictions, especially for small sample sizes. Furthermore, the Bayesian methods with informative priors performed better (smaller values of deviance information criterion (DIC)) than those with the non-informative priors. Therefore, our results demonstrated the advantages of applying the Bayesian methods on the SUR biomass models, not only obtaining better model fitting and predictions, but also offering the assessment and evaluation of the uncertainties for constructing and updating tree biomass models.

Short-rotation forestry is of interest to provide biomass for bioenergy and act as a carbon sink to mitigate global warming. The Poplar tree (Populus × xiaohei) is a fast-growing and high-yielding tree species in Northeast China. In this study, a total of 128 Populus × xiaohei trees from the Songnen Plain, Heilongjiang Province, Northeastern China, were harvested. Several available independent variables, such as tree diameter at breast height (D), tree’s total height (H), crown width (CW), and crown length (CL), were differently combined to develop three additive biomass model systems and eight stem volume models for Populus × xiaohei tree. Variance explained within the three additive biomass model systems ranged from 83% to 98%, which was lowest for the foliage models, and highest for the stem biomass models. Similar findings were found in the stem volume models, in which the models explained more than 94% of the variance. The additional predictors, such as H, CL, or CW, evidently enhanced the model fitting and performance for the total and components biomass along with the stem volume models. Furthermore, the biomass conversion and expansion factors (BCEFs) of the root (118.2 kg/m3), stem (380.2 kg/m3), branch (90.7 kg/m3), and foliage (31.2 kg/m3) were also calculated. The carbon concentrations of Populus × xiaohei in root, stem, branch, and foliage components were 45.98%, 47.74%, 48.32%, and 48.46%, respectively. Overall, the newly established models in this study provided complete and comprehensive tools for quantifying the biomass and stem volume of Populus × xiaohei, which might be essential to be specifically utilized in the Chinese National Forest Inventory.

The mixture of tree species has gradually become the focus of forest research, especially native species mixing. Mixed-species plantations of Manchurian ash (Fraxinus mandshurica Rupr.) and Changbai larch (Larix olgensis Henry) have successfully been cultivated in Northeast China. Height–diameter (H–D) models were found to be effective in designing the silvicultural planning for mixed-species plantations. Thus, this study aimed to develop a new system of H–D models for juvenile ash and larch mixed-species plantations, based on competition information and tree and stand attributes. The leave-one-out cross-validation was utilized for model validation. The result showed that the H–D relationship was affected not only by the tree attributes (i.e., tree size and competition information) but also by stand characteristics, such as site quality and species proportion of basal area. The best model explained more than 80% and 85% variation of the tree height of ash and larch, respectively. Moreover, model validation also confirmed the high accuracy of the newly developed model’s predictions. We also found that, in terms of total tree height, ash in middle rows were higher than those in side rows, while larch in the middle rows were higher in the early growth period but then became lower than those in the side rows, as the diameter increased. The newly established H–D models would be useful for forestry inventory practice and have the potential to aid decisions in mixed-species plantations of ash and larch.

Accurate quantification of tree biomass is critical and essential for calculating carbon storage, as well as for studying climate change, forest health, forest productivity, nutrient cycling, etc. Tree biomass is typically estimated using statistical models. Although various biomass models have been developed thus far, most of them lack a detailed investigation of the additivity properties of biomass components and inherent correlations among the components and aboveground biomass. This study compared the nonadditive and additive biomass models for larch (Larix olgensis Henry) trees in Northeast China. For the nonadditive models, the base model (BM) and mixed effects model (MEM) separately fit the aboveground and component biomass, and they ignore the inherent correlation between the aboveground and component biomass of the same tree sample. For the additive models, two aggregated model systems with one (AMS1) and no constraints (AMS2) and two disaggregated model systems without (DMS1) and with an aboveground biomass model (DMS2) were fitted simultaneously by weighted nonlinear seemingly unrelated regression (NSUR) and applied to ensure additivity properties. Following this, the six biomass modeling approaches were compared to improve the prediction accuracy of these models. The results showed that the MEM with random effects had better model fitting and performance than the BM, AMS1, AMS2, DMS1, and DMS2; however, when no subsample was available to calculate random effects, AMS1, AMS2, DMS1, and DMS2 could be recommended. There was no single biomass modeling approach to predict biomass that was best for all aboveground and component biomass except for MEM. The overall ranking of models based on the fit and validation statistics obeyed the following order: MEM > DMS1 > AMS2 > AMS1> DMS2 > BM. This article emphasized more on the methodologies and it was expected that the methods could be applied by other researchers to develop similar systems of the biomass models for other species, and to verify the differences between the aggregated and disaggregated model systems. Overall, all biomass models in this study have the benefit of being able to predict aboveground and component biomass for larch trees and to be used to predict biomass of larch plantations in Northeast China.

In this study, the effects of tree species, tissue types, and tree size on the carbon concentration were studied, and the two additive systems, one with tree diameter (D), and the other with both D and tree height (H), were developed to estimate the stem, root, branch, and foliage carbon content of 10 broadleaf species in northeast China. The coefficients of the two systems were estimated with the nonlinear seemingly unrelated regression (NSUR), while the heteroscedasticity of the model residual was solved with the weight function. Our results showed that carbon concentrations varied along with tree species and size; the tissues and foliage contained higher carbon concentration than other observed tissues. The two additive carbon equation systems exhibited good predictive and fitting performance, with Ra2 > 0.87, average prediction error of approximately 0, and small average absolute error and absolute error percentage. The carbon equation system constructed with D and H exhibited better fit and performance, particularly for the stem and total carbon. Thus, the additive carbon equation systems estimated the tree carbon of 10 broadleaf species more accurately. These carbon equations can be used to monitor the carbon pool sizes for natural forests in the Chinese National Forest Inventory.

Numerical integration of the instantaneous net photosynthetic rate (An) is a common method for calculating the long-term CO2 uptake of trees, and accurate dynamic simulation of the crown An has been receiving substantial attention. Tree characteristics are challenging to assess given their aerodynamically coarse crown properties, spatiotemporal variation in leaf functional traits and microenvironments. Therefore, the variables associated with the dynamic variations in the crown An must be identified. The relationships of leaf temperature (Tleaf), the vapor pressure deficit (VPD), leaf mass per area (LMA) and the relative depth into the crown (RDINC) with the parameters of the photosynthetic light-response (PLR) model of Larix olgensis Henry were analyzed. The LMA, RDINC and VPD were highly correlated with the maximum net photosynthetic rate (Amax). The VPD was the key variable that mainly determined the variation in the apparent quantum yield (AQY). Tleaf exhibited a significant exponential correlation with the dark respiration rate (Rd). According to the above correlations, the crown PLR model of L. olgensis trees was constructed by linking VPD, LMA and RDINC to the original PLR equation. The model performed well, with a high coefficient of determination (R2) value (0.883) and low root mean square error (RMSE) value (1.440 μmol m−2 s−1). The extinction coefficient (k) of different pseudowhorls within a crown was calculated by the Beer–Lambert equation based on the observed photosynthetically active radiation (PAR) distribution. The results showed that k was not a constant value but varied with the RDINC, solar elevation angle (ψ) and cumulative leaf area of the whole crown (CLA). Thus, we constructed a k model by reparameterizing the power function of RDINC with the ψ and CLA, and the PAR distribution within a crown was therefore well estimated (R2 = 0.698 and RMSE = 174.4 μmol m−2 s−1). Dynamic simulation of the crown An for L. olgensis trees was achieved by combining the crown PLR model and dynamic PAR distribution model. Although the models showed some weakened physiological biochemical processes during photosynthesis, they enabled the estimation of long-term CO2 uptake for an L. olgensis plantation, and the results could be easily fitted to gas-exchange measurements.

The spatial distribution of leaf area largely governs both the structure and function of a tree crown. Three sample trees were selected from a 16-year-old Larix olgensis plantation in the Maoershan Forest Farm, Heilongjiang Province, based on the average diameter at breast height in each plot. All needles from the branches in the nodal and internodal pseudowhorls within the crown were destructively sampled. The crown was divided into several segments in the vertical and horizontal directions, resulting in different sub-regions. The needle area (NA) in each sub-region was computed based on the needle mass per area (NMA). The vertical and horizontal distributions and their cumulative NA distributions were characterized using the Weibull distribution function and its cumulative form. A two-dimensional NA model was created by combining the two Weibull distribution functions of the vertical and horizontal distributions. The variation in the spatial distribution of the NA among the different crown directions is discussed, and the influence of competition from competitors on the spatial distribution of NA was analyzed. The results showed that the Weibull distribution function and its cumulative form performed well in describing the vertical and horizontal distributions and the cumulative distributions of the NA, which was generally concentrated upward within the crown. This phenomenon was most apparent in dense stands with strong competition. The center of the NA exhibited an inward shift in the horizontal direction within the crown but shifted outwards with an increase in competition. The cumulative vertical and horizontal distributions of the NA obviously varied with different crown directions, which was associated mainly with the light conditions. The two-dimensional NA patterns showed that the center of the NA generally shifted outward with an increase in the relative depth into the crown (RDINC), and that more concentrated and more skewed patterns usually occurred under increased competition. Different crown directions exhibited different two-dimensional NA patterns, but the core driver was the variable light condition caused by the competitors, particularly in closed stands.